|Title||The use of Bayesian hierarchical models for adaptive randomization in biomarker-driven phase II studies.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Barry, William T., Charles M. Perou, Kelly P Marcom, Lisa A. Carey, and Joseph G. Ibrahim|
|Journal||J Biopharm Stat|
|Keywords||Bayes Theorem, Biomarkers, Tumor, Clinical Trials, Phase II as Topic, Computer Simulation, Humans, Models, Statistical, Neoplasms, Predictive Value of Tests, Random Allocation, Randomized Controlled Trials as Topic, Sample Size, Treatment Outcome|
The role of biomarkers has increased in cancer clinical trials such that novel designs are needed to efficiently answer questions of both drug effects and biomarker performance. We advocate Bayesian hierarchical models for response-adaptive randomized phase II studies integrating single or multiple biomarkers. Prior selection allows one to control a gradual and seamless transition from randomized-blocks to marker-enrichment during the trial. Adaptive randomization is an efficient design for evaluating treatment efficacy within biomarker subgroups, with less variable final sample sizes when compared to nested staged designs. Inference based on the Bayesian hierarchical model also has improved performance in identifying the sub-population where therapeutics are effective over independent analyses done within each biomarker subgroup.
|Alternate Journal||J Biopharm Stat|
|Original Publication||The use of Bayesian hierarchical models for adaptive randomization in biomarker-driven phase II studies.|
|PubMed Central ID||PMC4459132|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P30 CA014236 / CA / NCI NIH HHS / United States
S10 RR025590 / RR / NCRR NIH HHS / United States
1S10RR025590-01 / RR / NCRR NIH HHS / United States
The use of Bayesian hierarchical models for adaptive randomization in biomarker-driven phase II studies.